Object detection involves determining whether particular objects are present in an image of a sequence of images. These images may be generated by sensors. For example, a camera may generate images in a video stream. One or more of these images may be processed to identify objects in the images.
In object detection, an image is processed to determine whether an object of a particular class is present. For example, a process may be employed to determine if humans are in the images. As another example, the process may determine if buildings, cars, airplanes, and/or other objects are present. Each of these types of objects represents a class. An object that is identified as being in the class is a member of the class.
Current approaches for detecting objects may have issues in identifying members of a class. For example, one object in an image may be falsely identified as a member of a class. In another example, current approaches may fail to identify an object in an image as a member of the class. Some current approaches may attempt to increase the speed that objects are identified. However, in increasing the speed, accuracy in correctly identifying the objects may decrease. In other examples, the amount of time to correctly identify the object may be greater than desired.
Accordingly, it would be advantageous to have a method and apparatus, which takes into account one or more of the issues discussed above as well as possibly other issues.